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Classification -- that is, identifying a label that applies to an item -- is a classic machine learning task that is very relevant to the messy data that we get from the web. This section explores the mathematical theory behind building a simple classifier.

Clustering is a classic unsupervised learning problem and is usually the first technique we reach for when using unlabeled data. We'll explore clustering using bookmark data, including clustering an entire dataset. Recommendation systems are a special case of the same problem, so we'll build one of those, too!

This section sums up the math, code, and data projects from the class.

00:11:30

Hilary Mason: An Introduction to Machine Learning with Web Data

Publisher: O'Reilly Media

Released: May 2011

Run
time: 2 hours 43 minutes

Once you've accumulated a pile of data through your web application, what do you do with it? In this insightful video course, bit.ly lead scientist Hilary Mason shows you how to solve data analysis problems using basic machine learning techniques and frameworks. You'll follow several examples through the entire process—from obtaining, cleaning, and exploring data to building a model and interpreting the results.

Examine several real-world analysis solutions, including supervised learning and classification, unsupervised learning and clustering, and building common machine learning applications such as recommendation systems. If you're a developer interested in the math and processes necessary to apply machine learning techniques to web data, this video course is for you.

Intended Audience:

This class is intended for developers who are interested in an introduction to the math and processes to apply machine learning techniques to web data.